Viral dynamic modeling of SARS-CoV-2 in non-human primates

Abstract

Non-human primates infected with SARS-CoV-2 exhibit mild clinical signs. Here we used a mathematical model to characterize in detail the viral dynamics in 31 cynomolgus macaques infected with 106 pfu of SARS-CoV-2 for which nasopharyngeal and tracheal viral load were frequently assessed. We identified that infected cells had a large daily viral production (>104 virus) and a within-host reproductive basic number of 6 and 4 in nasopharyngeal and tracheal compartment, respectively. After peak viral load, infected cells were rapidly cleared with a half-life of 9 hours, with no significant association between cytokine elevation and clearance. Translating our model to the context of human-to-human infection, human mild infection may be characterized by a peak occurring 4 days after infection, a viral shedding of ~11 days and a generation time of 4 days. These results improve the understanding of SARS-CoV-2 viral replication and better understand the infection to SARS-CoV-2 in humans.

Modeling SARS-CoV-2 viral kinetics and association with mortality in hospitalized patients from the French COVID cohort

“…Results obtained in previous reports suggested lower levels of R 0 , in a range 5 to 15 ( 28 – 30 ). In nonhuman primates, which do not show severe infection, we estimated R 0 in the nasopharynx to be about 5.6 (95% CI: 1.3 to 21) ( 31 ). Whether the high value found here is a consequence of the disease severity of our population as compared to other reports or is artifactual due to the limited information in the very early phase of the disease will require more investigation.…”

“…This assumption stems from the fact that there are epithelial cells in the upper respiratory tract (URT), that the URT has a volume of 30 mL, and that 1% of cells express ACE2 and TMPSS receptor ( 58 ), which are used by the virus to bind target cells. The proportion of infectious viruses was fixed to 10 −4 of the total RNA viral load, which was the upper bound found in animal models ( 31 ).…”

Mechanistic Modeling of SARS‐CoV‐2 and Other Infectious Diseases and the Effects of Therapeutics

“…58 With the availability of clinical and experimental data, many mathematical models have been developed and calibrated with data. 43,44,45,46,47,48,49,50,51,52,53 Overall, these studies greatly advanced our quantitative understanding of SARS-CoV-2 infection in both humans and nonhuman primates, the resulting immune responses, and the impact of therapeutics.…”

“…Viral dynamic models for chronic infections above can be adapted to quantify dynamics of acute infections, such as influenza, 24,40 West Nile virus, 41 respiratory synctial virus, 42 Zika, 26 and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). 43,44,45,46,47,48,49,50,51,52,53 In particular, extensive modeling efforts have been made towards understanding influenza infection and the immune response against it. These studies often serve as the basis for modeling other acute infections.…”

“…49 Models including a URT and an LRT have also been used to understand viral dynamics in nonhuman primates where viral load measurements were taken from both nasal swabs and bronchoalveolar lavage. 46,49,53 This type of model allows one to investigate the relationship between virus replication in different tissue compartments. For example, we have found that the rate of virus replication is positively correlated in the URT and the LRT 49 in the patients studied in Wolfel et al 57 The rate of transport of virus from the URT to the LRT is probably low as mucociliary clearance acts to bring particles out of the LRT.…”